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Random sample

A sample is a subject chosen from a population for investigation. A random sample is one chosen by a method involving an unpredictable component. Random sampling can also refer to taking a number of independent observations from the same probability distribution, without involving any real population. The sample usually is not a representative of the population from which it was drawn&mdash; this random variation in the results is known as sampling error. In the case of random samples, mathematical theory is available to assess the sampling error. Thus, estimates obtained from random samples can be accompanied by measures of the uncertainty associated with the estimate. This can take the form of a standard error, or if the sample is large enough for the central limit theorem to take effect, confidence intervals may be calculated.

Types of random sample

A simple random sample is hi selected so that all samples of the same size have an equal chance of being selected from the population.

A self-weighting sample, also known as an EPSEM (Equal Probability of Selection Method) sample, is one in which every individual, or object, in the population of interest has an equal opportunity of being selected for the sample. Simple random samples are self-weighting.

Stratified sampling involves selecting independent samples from a number of subpopulations, group or strata within the population. Great gains in efficiency are sometimes possible from judicious stratification.

Cluster sampling involves selecting the sample units in groups. For example, a sample of telephone calls may be collected by first taking a collection of telephone lines and collecting all the calls on the sampled lines. The analysis of cluster samples must take into account the intra-cluster correlation which reflects the fact that units in the same cluster are likely to be more similar than two units picked at random.

Methods of producing random samples

Physical randomization devices such as coins, playing cards or sophisticated devices such as ERNIE

Simple random sample - Wikipedia, the free encyclopedia

In statistics, a simple random sample is a subset of individuals (a sample) ...

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simple random sample

From Yahoo Answers

Question:At a large university a simple random sample of 5 female professors is selected and a simple random sample of 10 male professors is selected. The two samples are combined to give an overall sample of 15 professors. Te overall sample is
1.a simple random sample
2.biased
3.a stratified sample
4.none of the above
To select a sample of undergraduate students in the United States, I select a simple random sample of four states. From each of these states, I select a simple random sample of two colleges or universities. Finally, form each of these eight colleges or universities; I select a simple random sample of 20 undergraduates. My final sample consists of160 undergraduates. This is an example of
1.simple random sampling
2.stratified random sampling
3.multistage sampling
4. convenience sampling

Answers:The first is an example of: 3. a stratified sample.
The second is an example of: 3. multistage sampling.

Question:I know what the definition of each is, but I don't know how to figure it out?
For example:
A container holds 1000 thoroughly mixed items, and 100 items are selected for testing.

Answers:A simple random sample is a specific kind of random sample. A random sample uses randomization to pick your sample. That can be done in a number of ways. A simple random sample is basically like selecting names from a hat. Every subject in the population has an equally likely chance of being in the sample, and every possible sample has an equally likely chance of being selected.. Other possible ways to take a random sample are systematic sampling (where you randomly select a starting point, and then pick every nth subject), cluster sampling (where subjects are separated into clusters, then all of the subjects in a random sample of clusters are put in the sample), and stratified sampling (where subjects are put into strata, and then a random sample is taken from each strata).
Non random samples do not use randomization. Online polls are an example of a nonrandom sample. The person taking the sample does not take an effort to pick the sample; people choose themselves to be in the sample.
Your example is a simple random sample. It's just like drawing from a hat.
In short, simple random samples are random samples, but random samples are not necessarily simple random samples.

Question:I have both definitions but I'm still pretty confused. Can somebody please give me an example of how to use each one so I understand the difference?

Answers:ANSWER: Random Sampling vs. Simple Random Sampling is the two have negligibly different definitions. They're the same thing.
Random Sampling is the practice concerned with the selection of individuals intended to yield "some knowledge"
Simple Random Sampling is the practice of selecting individuals entirely by chance such that each individual is chosen from a larger set of a population.

Question:A simple random sample will be obtained from a normally distributed population. Find the minimum sample size needed to be 99% confident that the sample variance is within 40% of the population variance. Is such a sample size practical in most cases?
The minimum sample size needed is? ____
Is the sample size practical?
Can someone help me work out this problem? Thanks so much.

Answers:You need some strong result on the error in the standard variance estimator. I know only of bounds on this.
If you asked about the sample *mean* that would be much easier.
Maybe you could explore it with computer simulation? Is it really independent of the population variance?